Details
| Original language | English |
|---|---|
| Article number | 12527 |
| Journal | Sustainability (Switzerland) |
| Volume | 13 |
| Issue number | 22 |
| Publication status | Published - 12 Nov 2021 |
Abstract
This study analyzes the temporally resolved location and trip data of shared e-scooters over nine months in Berlin from one of Europe’s most widespread operators. We apply time, distance, and energy consumption filters on approximately 1.25 million trips for outlier detection and trip categorization. Using temporally and spatially resolved trip pattern analyses, we investigate how the built environment and land use affect e-scooter trips. Further, we apply a density-based clustering algorithm to examine point of interest-specific patterns in trip generation. Our results suggest that e-scooter usage has point of interest related characteristics. Temporal peaks in e-scooter usage differ by point of interest category and indicate work-related trips at public transport stations. We prove these characteristic patterns with the statistical metric of cosine similarity. Considering average cluster velocities, we observe limited time-saving potential of e-scooter trips in congested areas near the city center.
Keywords
- Big data, E-scooter, HDBSCAN, Land use analysis, Micro-mobility, Shared-mobility, Spatial allocation, Spatiotemporal analysis
ASJC Scopus subject areas
- Social Sciences(all)
- Geography, Planning and Development
- Energy(all)
- Renewable Energy, Sustainability and the Environment
- Environmental Science(all)
- Environmental Science (miscellaneous)
- Energy(all)
- Energy Engineering and Power Technology
- Environmental Science(all)
- Management, Monitoring, Policy and Law
Sustainable Development Goals
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Sustainability (Switzerland), Vol. 13, No. 22, 12527, 12.11.2021.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A spatiotemporal study and location-specific trip pattern categorization of shared e-scooter usage
AU - Heumann, Maximilian
AU - Kraschewski, Tobias
AU - Brauner, Tim
AU - Tilch, Lukas
AU - Breitner, Michael H.
N1 - Funding Information: Funding: The publication of this article was funded by the Open Access Fund of Leibniz Universität Hannover.
PY - 2021/11/12
Y1 - 2021/11/12
N2 - This study analyzes the temporally resolved location and trip data of shared e-scooters over nine months in Berlin from one of Europe’s most widespread operators. We apply time, distance, and energy consumption filters on approximately 1.25 million trips for outlier detection and trip categorization. Using temporally and spatially resolved trip pattern analyses, we investigate how the built environment and land use affect e-scooter trips. Further, we apply a density-based clustering algorithm to examine point of interest-specific patterns in trip generation. Our results suggest that e-scooter usage has point of interest related characteristics. Temporal peaks in e-scooter usage differ by point of interest category and indicate work-related trips at public transport stations. We prove these characteristic patterns with the statistical metric of cosine similarity. Considering average cluster velocities, we observe limited time-saving potential of e-scooter trips in congested areas near the city center.
AB - This study analyzes the temporally resolved location and trip data of shared e-scooters over nine months in Berlin from one of Europe’s most widespread operators. We apply time, distance, and energy consumption filters on approximately 1.25 million trips for outlier detection and trip categorization. Using temporally and spatially resolved trip pattern analyses, we investigate how the built environment and land use affect e-scooter trips. Further, we apply a density-based clustering algorithm to examine point of interest-specific patterns in trip generation. Our results suggest that e-scooter usage has point of interest related characteristics. Temporal peaks in e-scooter usage differ by point of interest category and indicate work-related trips at public transport stations. We prove these characteristic patterns with the statistical metric of cosine similarity. Considering average cluster velocities, we observe limited time-saving potential of e-scooter trips in congested areas near the city center.
KW - Big data
KW - E-scooter
KW - HDBSCAN
KW - Land use analysis
KW - Micro-mobility
KW - Shared-mobility
KW - Spatial allocation
KW - Spatiotemporal analysis
UR - http://www.scopus.com/inward/record.url?scp=85119191889&partnerID=8YFLogxK
U2 - 10.3390/su132212527
DO - 10.3390/su132212527
M3 - Article
AN - SCOPUS:85119191889
VL - 13
JO - Sustainability (Switzerland)
JF - Sustainability (Switzerland)
SN - 2071-1050
IS - 22
M1 - 12527
ER -